Anomaly detection of semiconductor processing equipment using equipment behaviour

Autor:	Toshiya Hirai, Yuki Shiga, Mitsuru Shimizu, Eiji Imura, Manabu Kano
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	semiconductor process equipment deposition cluster analysis anomaly detection Control engineering systems. Automatic machinery (General) TJ212-225
Zdroj:	SICE Journal of Control, Measurement, and System Integration, Vol 16, Iss 1, Pp 332-337 (2023)
Druh dokumentu:	article
ISSN:	1884-9970 18824889
DOI:	10.1080/18824889.2023.2279338
Popis:	As semiconductor design rules evolve, the required level of reliability for semiconductor processing equipment is increasing. It is impossible to detect anomalies simply by checking a single factor, the oxygen concentration, which is the most important indicator of the equipment performance. We extracted 16 features from the behaviour of oxygen concentration and pressure in the load area, and built univariate and multivariate models by using logistic regression with these features. The proposed method was able to detect anomalous equipment that could not be detected by monitoring only the oxygen concentration, and greatly shortened the processing lead time including adjustment.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/9918309fdc3c471c8b225cac0653f347 Zobrazit plný text záznamu View record in DOAJ